Resource-efficient photonic networks for next-generation AI computing
Abstract Current trends in artificial intelligence toward larger models demand a rethinking of both hardware and algorithms. Photonics-based systems offer high-speed, energy-efficient computing units, provided algorithms are designed to exploit photonics’ unique strengths. The recent implementation...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2025-01-01
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| Series: | Light: Science & Applications |
| Online Access: | https://doi.org/10.1038/s41377-024-01717-6 |
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| _version_ | 1841559046157500416 |
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| author | Ilker Oguz Mustafa Yildirim Jih-Liang Hsieh Niyazi Ulas Dinc Christophe Moser Demetri Psaltis |
| author_facet | Ilker Oguz Mustafa Yildirim Jih-Liang Hsieh Niyazi Ulas Dinc Christophe Moser Demetri Psaltis |
| author_sort | Ilker Oguz |
| collection | DOAJ |
| description | Abstract Current trends in artificial intelligence toward larger models demand a rethinking of both hardware and algorithms. Photonics-based systems offer high-speed, energy-efficient computing units, provided algorithms are designed to exploit photonics’ unique strengths. The recent implementation of cellular automata in photonics demonstrates how a few local interactions can achieve high throughput and precision. |
| format | Article |
| id | doaj-art-1e113f2a97db4c8b92220733c6dcb556 |
| institution | Kabale University |
| issn | 2047-7538 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Light: Science & Applications |
| spelling | doaj-art-1e113f2a97db4c8b92220733c6dcb5562025-01-05T12:46:50ZengNature Publishing GroupLight: Science & Applications2047-75382025-01-011411410.1038/s41377-024-01717-6Resource-efficient photonic networks for next-generation AI computingIlker Oguz0Mustafa Yildirim1Jih-Liang Hsieh2Niyazi Ulas Dinc3Christophe Moser4Demetri Psaltis5EPFL, Institute of Electrical and Micro EngineeringEPFL, Institute of Electrical and Micro EngineeringEPFL, Institute of Electrical and Micro EngineeringEPFL, Institute of Electrical and Micro EngineeringEPFL, Institute of Electrical and Micro EngineeringEPFL, Institute of Electrical and Micro EngineeringAbstract Current trends in artificial intelligence toward larger models demand a rethinking of both hardware and algorithms. Photonics-based systems offer high-speed, energy-efficient computing units, provided algorithms are designed to exploit photonics’ unique strengths. The recent implementation of cellular automata in photonics demonstrates how a few local interactions can achieve high throughput and precision.https://doi.org/10.1038/s41377-024-01717-6 |
| spellingShingle | Ilker Oguz Mustafa Yildirim Jih-Liang Hsieh Niyazi Ulas Dinc Christophe Moser Demetri Psaltis Resource-efficient photonic networks for next-generation AI computing Light: Science & Applications |
| title | Resource-efficient photonic networks for next-generation AI computing |
| title_full | Resource-efficient photonic networks for next-generation AI computing |
| title_fullStr | Resource-efficient photonic networks for next-generation AI computing |
| title_full_unstemmed | Resource-efficient photonic networks for next-generation AI computing |
| title_short | Resource-efficient photonic networks for next-generation AI computing |
| title_sort | resource efficient photonic networks for next generation ai computing |
| url | https://doi.org/10.1038/s41377-024-01717-6 |
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